Carcinogenesis is a multi-step process involving genetic alterations and non-genotoxic mechanisms. The in vitro cell transformation assay allows the monitoring of the neoplastic phenotype by foci formation in suitable cells (e.g. C3H10T1/2 mouse embryo fibroblasts) showing aberrant morphology of massive build-up, polar and multi-layered densely stained cells. The classification of transformed foci in C3H cells relies on light microscopy scoring by a trained human expert based on standard rules. This procedure is time-consuming and prone, in some cases, to subjectivity, thereby leading to possible over- or under-estimation of the carcinogenic potential of tested compounds. Herewith we describe the in vitro neoplastic transformation induced by B[a]P and CdCl2, and the development of a foci classifier based on image analysis and statistical classification. The image analysis system, which relies on 'spectrum enhancement', is quantitative and extracts descriptors of foci texture and structure. The statistical classification method is based on the Random Forest algorithm. We obtained a classifier trained by using expert's supervision with a 20% classification error. The proposed method could serve as a basis to automate the in vitro cell transformation assay.